Identification of best practices in the oil and gas operations is gaining unprecedented momentum. This is partly due to the realities of the new economy that ties the success of oil and gas companies to their performance in the stock market. Companies that have gathered large amounts of data now realize that they own a valuable commodity (above and beyond the hydrocarbon) that can play an important role in increasing efficiency in their day to day operations.
The question is how this vast amount of data can be used in order to help the company's bottom-line. This paper attempts to address this question by introducing a newly developed methodology that enables oil and gas companies to deduce information and knowledge from the existing data. The deduced information and knowledge can then be used in developing business rules and making decisions.
Many companies in the oil and gas industry have been collecting large amounts of data over the past several years. Hundreds of thousands of dollars have been invested in collecting and compiling various types of data. These databases cover all aspects of oil and gas business, from purely technical data that includes certain measurements from the reservoir or the surface facilities to non-technical data such as those related to economics or human resources issues. Now that all this data is available, following questions may be asked:
- "What can we do with this data?"
- "How can the company get a return on its data collection and preservation investment?"
- "Are there stories hidden in the megabytes, or sometime gigabytes of data?"
- "The collected data is a reflection of the history of the operations that have taken place and sometime are still taking place. What can we learn from our past practices?"
As the volume of data increases, human cognition is no longer capable of deciphering important information from it by conventional techniques. Data mining and machine learning techniques must be used in order to deduce information and knowledge from the raw data that resides in the databases. The Intelligent Best Practices Analysis (IBPA) that is introduced here incorporates the state of the art in data mining and machine learning to assist petroleum professionals in making the most of their existing data. Figure 1 is a schematic diagram of IBPA.
Figure 1. Flow Chart for the Intelligent Best Practices Analysis.
